US8107721B2 - Method and system for determining poses of semi-specular objects - Google Patents
Method and system for determining poses of semi-specular objects Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
- G06T7/521—Depth or shape recovery from laser ranging, e.g. using interferometry; from the projection of structured light
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/50—Depth or shape recovery
- G06T7/55—Depth or shape recovery from multiple images
- G06T7/586—Depth or shape recovery from multiple images from multiple light sources, e.g. photometric stereo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10141—Special mode during image acquisition
- G06T2207/10152—Varying illumination
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- the invention relates generally to computer vision, and more particularly to determining poses of semi-specular objects.
- a system for automated ‘bin-picking’ in a factory can acquire 3D data of a bin containing multiple instances of the same object, and compare the 3D data with a known 3D model of the object, in order to determine the poses of objects in the bin. Then, a robot arm can be directed to retrieve a selected one of the objects.
- the pose of an object is its 3D location and 3D orientation at the location.
- One set of vision-based techniques for sensing 3D data assumes that the objects have non-specular surfaces, such as matte surfaces.
- Another type of sensor determines the silhouette of the object, and compares the silhouette with the known 3D model of the object, in order to determine pose.
- One set of techniques for determining the silhouette assumes that, the objects cast shadows when illuminated.
- Vision-based techniques for sensing 3D data for non-specular surfaces include structured light, time-of-flight laser scanners, stereo cameras, moving cameras, photometric stereo, shape-from -shading, and depth-from-(de)focus.
- Vision-based techniques for sensing the 3D pose and shape of specular surfaces assume that there are features in a surrounding scene that are reflected by the specular surface.
- the features may be sparse, such as specular highlights arising: from point light sources in the scene. If the features are sparse, then the sensed 3D shape of the surface is also sparse. This is undesirable for many applications. For example, it is difficult to determine a reliable pose of an object when the sensed features are sparse.
- the problem can be ameliorated by moving the camera or the identifying features relative to the surface, but this increases the complexity of the system and is time-consuming.
- the embodiments of the invention provide a method for determining a pose of a semi-specular object using a hybrid sensor including a laser scanner and a multi-flash camera (camera).
- the scanner and camera have complementary capabilities.
- the laser scanning acquires high-quality 3D coordinate data of fronto-parallel parts of the surface of an object:, with quality decreasing as the surface becomes more oblique with respect to the scanner; the laser scanning cannot: acquire any data at the occluding contour of the object.
- the camera can acquire 2D flash images that show cast-shadows of the object, which can be used to determine the silhouette, but it does not acquire data elsewhere on the object surface.
- the ability to identify cast-shadows in the flash images decreases as the background objects on which the shadows are being cast become more specular
- both the scanner and the camera produce lower-quality information on semi-specular objects than on diffuse-surface objects.
- the method combines the 3D data and the 2D silhouette information, so that even though the information are poor quality when taken individually, it is still possible to obtain an accurate pose of a semi-specular object when taking them together,
- a camera acquires a set of coded images and a set of flash images of an object.
- the coded images are acquired while scanning the object with a laser beam pattern
- the flash images are acquired while illuminating the object with a set of light sources at different locations near the camera, there being one flash image for each fight source
- 3D coordinates of points on the surface of the object are determined from the set of coded images
- 2D silhouettes of the object are determined from shadows cast in the set of flash images
- Surface normals are obtained using photometric stereo with the flash images. The 3D coordinates, 2D silhouettes and surface normals are used to determine the pose of the object.
- FIG. 1 is a block diagram of a system and method for determining a 3D pose of an object that includes specular surfaces according to an embodiment of our invention
- FIG. 2 is a schematic of a camera and a light source relative to a surface according to an embodiment of our invention.
- FIG. 3 is an image of occluded contours of an object surfaces according to an embodiment of our invention.
- FIG. 1 shows a system and method 100 for determining a 3D pose 101 of an object 130 that includes semi-specular surfaces according to an embodiment of our invention.
- the 3D pose as defined herein means the 3D location and the 3D orientation of the object.
- the system includes a hybrid sensor including a laser scanner 110 and a camera 120 .
- the laser scanner 110 emits a laser beam 111 in a pattern 112 that can be used to determine 3D range data in a set of coded images 326 acquired by the camera 120 .
- the pattern can use Gray-codes so that the pattern at each point on the surface of the object is unique.
- the method determines 3D coordinate data at each point on the surface from the set of coded images 126 .
- the camera also 120 acquires light 121 reflected by the object.
- the camera includes multiple flash units 125 , e.g., LEDs, arranged at different locations, e.g., in an octagon or circular pattern, around the camera.
- the LEDs are bright point light sources that cast sharp shadows.
- the camera also acquires a set of flash images of the object. The flash images are used to determine the 2D silhouette of the object.
- the set of coded images 126 is used to determine the 3D coordinates of the points 102 identified by laser scanning as well the 2D silhouette 103 .
- the significance is that the 3D points and the 2D silhouettes are measured from a single camera 120 so they are in the same coordinate frame. This makes it possible to project the 3D points to the 2D camera image plane. Alternatively, it is also possible to ‘back-project’ any point on a 2D silhouette to a 3D ray in 3D space, where it is in the same coordinate frame as the 3D point coordinates obtained by the laser scanning.
- the laser scanning projects the laser beam pattern onto the surface of the object to acquire ranges or ‘depths’ to points 301 on the object's surface.
- the laser scanning data are sometimes called a range or depth map.
- the range map can be converted to the coordinates 102 of 3D points 301 on the surface of the object.
- the camera also acquires a diffuse component of light reflected by the surface of the object by acquiring the set of flash images 127 , one for each point light source 125 .
- the light sources cast shadows at occluding contours, which reveal the silhouette of the object.
- the laser scanning is less effective if the surface is specular because the reflected laser light shifts from the diffuse component to a specular component. This makes it more difficult to detect the diffuse component at the camera.
- laser scanning is only effective for the parts of the surface that are most fronto-parallel to the sensor. It is difficult to extract data for oblique parts of the surface. On a curved object, only a small amount of surface data can be determined.
- the object is also illuminated by the point light sources (flash units) 125 arranged near the camera.
- the corresponding image 127 includes the shadows cast by the object onto a nearby background surface.
- the cast shadows are used to infer the 2D occluding and self-occluding silhouettes as observed from the viewpoint of the camera.
- the occluding contour is obtained most reliably when the shadows are being cast on a diffuse surface, which is the case for an isolated object of any material on a diffuse background and not for external distribution, or for stacked diffuse objects.
- Our goal is to determine the 3D pose for each of an arbitrarily stacked pile of semi-specular objects in a bin 135 . It is assumed that objects are identical and all have the same known shape. For a particular object shape, and a full spectrum of possible materials with Lambertian to mirror-surface, there will be some failures as the object material becomes more specular, beyond which the camera cannot extract sufficient data to determine the pose.
- the coded images 126 produces high quality 3D coordinate information at semi-specular surfaces front-to-parallel to the scanner but no information at the occluding contours, while the flash images 127 produces shape information only at occluding contours.
- the scanner and the camera are complementary and mutually supporting.
- the coded images produce the 3D coordinates 102 of the points 131 , while the flash image produces the silhouette data 103 . Therefore, the acquired 2D and 3D data are heterogeneous,
- the laser scanning uses structured light based on Gray-codes as described by Scharstein et al., “High-accuracy stereo depth maps using structured light,” Proc, Conference Determiner Vision and Pattern Recognition, 2003.
- the camera is described by Raskar et al., “Non-photorealistic camera: Depth edge detection and stylized rendering using multi-flash imaging,” ACM Siggraph, 2004 and U.S. Pat. No. 7,295,720.
- the method of pose computation is based on range map matching described by Germann et al, “Automatic pose estimation for range images on the GPU, Sixth Intl Conf on Digital Imaging and Modeling, 2007, and in U.S.
- Calibration is a one-time preprocessing step. Calibration of the sensors can use a second, temporary camera 140 . This is not essential but simplifies the processing of the data.
- the calibration determines the intrinsic and extrinsic stereo parameters of the laser scanner 110 and the camera 120 .
- the extrinsic parameters between the camera and the laser scanner We project the pattern on the blank surface and store corresponding points in the camera image and on the laser scanning image plane. We repeat the above for two or more positions of the plane and determine a fundamental matrix F between the camera and scanner.
- the fundamental matrix F is a 3 ⁇ 3 matrix, which relates corresponding points in stereo images.
- the above steps provide a complete calibration of all intrinsics and extrinsics parameters for all optical components. This calibration information is used to determine 3D surface points using Gray-codes,
- Our hybrid sensor combines 110 the data acquired from the laser scanning 110 and flash images 103 .
- the data are used to determine coordinates 102 of the 3D points on the object 130 , and to determine the silhouettes 103 of the occluding contours 300 of objects in the bin, see FIG. 3 for an example object with complex contours. It also enables us to determine surface normals n 104 at the 3D points using photometric stereo.
- the normals 104 indicate the orientation of the object.
- Our method differs from conventional photometric stereo in that there is no need for an assumption that the light sources 125 are distant from the object 130 , which is an issue in practical applications, such bin picking.
- the camera 120 observes a 3D point X 131 on the surface 132 of the object 130 , with coordinates 102 known from the laser scanning, and records intensity I 0 .
- the first LED 125 is illuminated, and the camera records intensity I 1 .
- This puts a constraint on the surface normal I 1 ⁇ I 0 kv, n, (1) where k is an unknown constant depending on the brightness of the LED, and the surface albedo at the point X. Brightness is assumed to be constant for all the LEDS, and hence k is also constant.
- Each LED can be used to generate one equation, and three or more equations provide a linear solution for the orientation of the normal n, up to unknown scale, which can be normalised to obtain a unit vector.
- the laser scanning produces the coordinates 102 of the 3D points from which surface normals n can be inferred.
- photometric stereo produces a per-pixel measurement at the camera, whereas 3D points require local surface fitting to generate a normal, which is a non-trivial process.
- FIG. 3 shows example occluding contours 300 for a complex object.
- the pose determination can be done in 3D or 2D.
- For computational, efficiency we perform all operations on in 2D on the image plane. Because the data may be incomplete, our method assumes that the occluding contours can also be incomplete.
- Germann The input for the method by Germann is a 3D range map, and the pose determination, is a minimization over the six DOF of pose of a 3D distance error, to bring the object model into close correspondence with the range data.
- the distance error of Germann significantly to work on our 2D image plane, and to include an error associated with the occluding contours. Note, Germann only considers 3D range data and not 2D images.
- the 3D model of the object can be obtained by computer-aided design (CAD).
- CAD computer-aided design
- the 3D model of the object is matched to the 2D images 127 , and consistency is measured in 2D on the image plane which has both the 3D laser data and the 2D contour data.
- the object model is projected onto the image plane.
- the projected information defines a silhouette and also provides depth and surface normal information for pixels inside the contour of the object.
- Our cost function has two components: a position error D 1 for the projected model and the laser scanning 3D coordinate data 102 ; and a shape error D 2 for the projected model and the occluding 2D contours.
- the set of pixels corresponding to the projected model of the object is P.
- the depth and surface normal of the object: model are known at every pixel in the set P.
- the set of pixels where the laser scanning has acquired coordinate data is L.
- the depth of the object is known at each pixel in the set L.
- the surface normal of the target object is typically known at each pixel in the set L, but may be absent if the photometric stereo failed.
- the position error D 1 is measured over the pixels in the intersection of the sets P and L.
- the shape error D 2 measures a consistency between the boundary of
- the shape error D 2 is a 3D error, so that it can be meaningfully summed with the position error D 1 .
- the pixels b on a surface or boundary of the projected 3D model is a set B.
- the pixels m where the camera has detected an occluding contour is a set M.
- Each pixel m in the set M is paired with a closest pixel b in the set M.
- the set of pairs (b, m) is culled in two ways
- the shape error D 2 is summed over the resulting set of pairs (m, b).
- the pixel-specific depth d can be replaced by a global value d 0 that is the average distance to the 3D points 131 .
- a problem in using a laser scanner on a specular object is caused by inter-reflection, whether between the object and the background, or between objects.
- the detected signal is still a valid Gray-code, but the path of the light was not directly from the laser to the surface and back, so triangulation of the range data generates a spurious 3D point.
- Both methods will be inconsistent in an area where there is an inter-reflection. Both methods may detect a signal for the inter-reflection, but their respective 3D computations are based on different light sources, i.e., the laser and the LEDs, so the spurious 3D points and spurious photometric surface normals generated for inter-reflection are not consistent. Inconsistent areas are eliminated from the pose determination.
- a hybrid sensor system and method determines a pose of a semi-specular object.
- the method combines data from a laser scanning and from a multi-flash images.
- the method deals with dealing with inter-reflections when scanning specular objects.
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Abstract
Description
I 1 −I 0 =kv, n, (1)
where k is an unknown constant depending on the brightness of the LED, and the surface albedo at the point X. Brightness is assumed to be constant for all the LEDS, and hence k is also constant. Each LED can be used to generate one equation, and three or more equations provide a linear solution for the orientation of the normal n, up to unknown scale, which can be normalised to obtain a unit vector. This scheme fails if the surface is specular at the
e 1=(r 1 −r 2)·λ, (2)
where ri is the depth and λ is unity if the scanning process failed to determine surface normal at the pixel, else
λ=1.0/max(cos 45, n 1 , n 2), (3)
where n1 and n2 are the surface normals of the object model and the object at the pixel.
e 2 =d·tan θ, (4)
where d is the distance to the object model at pixel b, and θ is the angle between the two camera rays through pixels m and b. For computational efficiency, the pixel-specific depth d can be replaced by a global value d0 that is the average distance to the 3D points 131.
Claims (10)
e 1=(r 1 −r 2)·λ,
λ=1.0/max(cos 45, n1, n2),
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US20090297020A1 (en) | 2009-12-03 |
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